Institute of Medicine, Board on Population Health and Public Health Practice, & Committee on Women’s Health Research. Women’s Health Research: Progress, Pitfalls, and Promise (National Academies Press (US), Washington (DC), 2010).
Institute of Medicine (US) Committee on Understanding the Biology of Sex and Gender Differences. Exploring the Biological Contributions to Human Health: Does Sex Matter? (National Academies Press (US), Washington (DC), 2001).
Office of Research on Women’s Health. History of Women’s Participation in Clinical Research. https://orwh.od.nih.gov/toolkit/recruitment/history (2019).
Institute of Medicine (US) Committee on Women’s Health Research. Introduction. In Women’s Health Research: Progress, Pitfalls, and Promise. (ed. Grossblatt, N.) (National Academies Press (US), Washington, DC, 2010).
Smith, K. Women’s Health Research Lacks Funding—these Charts Show How. https://www.nature.com/immersive/d41586-023-01475-2/index.html (2023).
Mirin, A. A. Gender disparity in the funding of diseases by the U.S. National Institutes of Health. J. Womens Health 2002 30, 956–963 (2021).
Google Scholar
Fisk, N. & Atun, R. Systematic analysis of research underfunding in maternal and perinatal health. BJOG Int. J. Obstet. Gynaecol 116, 347–356 (2009).
Google Scholar
Rice, L. W. et al. Increasing NIH funding for academic departments of obstetrics and gynecology: a call to action. Am. J. Obstet. Gynecol. 223, 79.e1–79.e8 (2020).
Google Scholar
Giudice, L. C. Clinical practice. Endometriosis. N. Engl. J. Med. 362, 2389–2398 (2010).
Google Scholar
Bunis, D. G. et al. Whole-tissue deconvolution and scRNAseq analysis identify altered endometrial cellular compositions and functionality associated with endometriosis. Front. Immunol. 12, 788315 (2022).
Google Scholar
Oskotsky, T. T. et al. Identifying therapeutic candidates for endometriosis through a transcriptomics-based drug repositioning approach. iScience 109388 https://doi.org/10.1016/j.isci.2024.109388 (2024).
Blencowe, H. et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet Lond. Engl. 379, 2162–2172 (2012).
Google Scholar
Vora, B. et al. Meta-analysis of maternal and fetal transcriptomic data elucidates the role of adaptive and innate immunity in preterm birth. Front. Immunol. 9, 993 (2018).
Google Scholar
Le, B. L., Iwatani, S., Wong, R. J., Stevenson, D. K. & Sirota, M. Computational discovery of therapeutic candidates for preventing preterm birth. JCI Insight 5, e133761, 133761 (2020).
Google Scholar
Zhang, G. et al. Genetic associations with gestational duration and spontaneous preterm birth. N. Engl. J. Med. 377, 1156–1167 (2017).
Google Scholar
Panagopoulos Abrahamsson, D. et al. A comprehensive non-targeted analysis study of the prenatal exposome. Environ. Sci. Technol. 55, 10542–10557 (2021).
Google Scholar
Knijnenburg, T. A. et al. Genomic and molecular characterization of preterm birth. Proc. Natl Acad. Sci. USA 116, 5819–5827 (2019).
Google Scholar
Kosti, I., Lyalina, S., Pollard, K. S., Butte, A. J. & Sirota, M. Meta-analysis of vaginal microbiome data provides new insights into preterm birth. Front. Microbiol. 11, 476 (2020).
Google Scholar
Huang, C. et al. Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth. BMC Biol. 21, 199 (2023).
Google Scholar
Minot, S. S. et al. MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies. Cell Rep. Methods 3, 100639 (2023).
Google Scholar
Golob, J. L. et al. Microbiome preterm birth DREAM challenge: crowdsourcing machine learning approaches to advance preterm birth research. Cell Rep. Med. 101350 https://doi.org/10.1016/j.xcrm.2023.101350 (2023).
DiGiulio, D. B. et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl Acad. Sci. USA 112, 11060–11065 (2015).
Google Scholar
Corwin, E. J. et al. Protocol for the Emory University African American vaginal, oral, and gut microbiome in pregnancy Cohort study. BMC Pregnancy Childbirth 17, 161 (2017).
Google Scholar
Ye, C. et al. The periodontopathic bacteria in placenta, saliva and subgingival plaque of threatened preterm labor and preterm low birth weight cases: a longitudinal study in Japanese pregnant women. Clin. Oral Investig. 24, 4261–4270 (2020).
Google Scholar
Liao, J. et al. Microdiversity of the vaginal microbiome is associated with preterm birth. Nat. Commun. 14, 4997 (2023).
Google Scholar
Rana, S., Lemoine, E., Granger, J. P. & Karumanchi, S. A. Preeclampsia: pathophysiology, challenges, and perspectives. Circ. Res. 124, 1094–1112 (2019).
Google Scholar
Leavey, K. et al. Unsupervised placental gene expression profiling identifies clinically relevant subclasses of human preeclampsia. Hypertension Dallas, TX 1979 68, 137–147 (2016).
Google Scholar
Broekhuizen, M. et al. The placental innate immune system is altered in early-onset preeclampsia, but not in late-onset preeclampsia. Front. Immunol. 12, 780043 (2021).
Google Scholar
Callahan, T. J. et al. Knowledge-driven mechanistic enrichment of the preeclampsia ignorome. In Biocomputing 2023 (eds Altman, R. B. et al.) 371–382 (World Scientific, 2022).
Admati, I. et al. Two distinct molecular faces of preeclampsia revealed by single-cell transcriptomics. Medicine 4, 687–709.e7 (2023).
Google Scholar
The White House Office of the Press Secretary to President George W. Bush. A New Generation of American Innovation. https://georgewbush-whitehouse.archives.gov/infocus/technology/economic_policy200404/chap3.html (2004).
Adler-Milstein, J. & Jha, A. K. Sharing clinical data electronically: a critical challenge for fixing the health care system. JAMA 307, 1695–1696 (2012).
Google Scholar
All of Us Research Program NIH. All of Us Seeks Input on Broadening Participants’ Electronic Health Record Data. https://allofus.nih.gov/news-events/announcements/all-us-seeks-input-broadening-participants-electronic-health-record-data (2022).
Christ, J. P. et al. Incidence, prevalence, and trends in endometriosis diagnosis: a United States population-based study from 2006 to 2015. Am. J. Obstet. Gynecol. 225, 500.e1–500.e9 (2021).
Google Scholar
Shafrir, A. L. et al. Validity of self-reported endometriosis: a comparison across four cohorts. Hum. Reprod. 36, 1268–1278 (2021).
Google Scholar
Burton, C. et al. Pointers to earlier diagnosis of endometriosis: a nested case-control study using primary care electronic health records. Br. J. Gen. Pract. 67, e816–e823 (2017).
Google Scholar
Hsu, A. L. et al. Coronavirus disease 2019 (COVID-19) disease severity: pregnant vs. nonpregnant women at 82 facilities. Clin. Infect. Dis 74, 467–471 (2022).
Google Scholar
Molina, R. L. et al. Comparison of pregnancy and birth outcomes before vs. during the COVID-19 pandemic. JAMA Netw. Open 5, e2226531 (2022).
Google Scholar
Miller, M. J. et al. Impact of COVID-19 on cervical cancer screening rates among women aged 21–65 years in a large integrated health care system—Southern California, January 1–September 30, 2019, and January 1–September 30, 2020. Morb. Mortal. Wkly. Rep. 70, 109–113 (2021).
Google Scholar
Amit, G. et al. Antidepressant use during pregnancy and the risk of preterm birth – a cohort study. npj Womens Health 2, 1–7 (2024).
Google Scholar
Ross, L. E. et al. Selected pregnancy and delivery outcomes after exposure to antidepressant medication: a systematic review and meta-analysis. JAMA Psychiatry 70, 436–443 (2013).
Google Scholar
Eke, A. C., Saccone, G. & Berghella, V. Selective serotonin reuptake inhibitor (SSRI) use during pregnancy and risk of preterm birth: a systematic review and meta-analysis. BJOG Int. J. Obstet. Gynaecol. 123, 1900–1907 (2016).
Google Scholar
Abraham, A. et al. Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth. BMC Med. 20, 333 (2022).
Google Scholar
Huang, H. et al. Investigation of association between environmental and socioeconomic factors and preterm birth in California. Environ. Int. 121, 1066–1078 (2018).
Google Scholar
Oh, S. S. et al. Diversity in clinical and biomedical research: a promise yet to be fulfilled. PLoS Med. 12, e1001918 (2015).
Google Scholar
Ibrahim, H., Liu, X., Zariffa, N., Morris, A. D. & Denniston, A. K. Health data poverty: an assailable barrier to equitable digital health care. Lancet Digit. Health 3, e260–e265 (2021).
Google Scholar
Kons, K. M. et al. Exclusion of reproductive-aged women in COVID-19 vaccination and clinical trials. Women’s Health Issues 32, 557–563 (2022).
Google Scholar
Oskotsky, T. et al. Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students. PLoS Comput. Biol. 18, e1009719 (2022).
Google Scholar
Rothman, K. J. Epidemiology: An Introduction (Oxford University Press, 2012).
Innes, G. K. et al. The measurement error elephant in the room: challenges and solutions to measurement error in epidemiology. Epidemiol. Rev 43, 94–105 (2022).
Google Scholar
Greenland, S. & Morgenstern, H. Confounding in health research. Annu. Rev. Public Health 22, 189–212 (2001).
Google Scholar
Mahajan, R. et al. Standardized Protocol Items Recommendations for Observational Studies (SPIROS) for observational study protocol reporting guidelines: protocol for a Delphi Study. JMIR Res. Protoc. 9, e17864 (2020).
Google Scholar
Ehrenstein, V. et al. Helping everyone do better: a call for validation studies of routinely recorded health data. Clin. Epidemiol. 8, 49–51 (2016).
Google Scholar
Bolignano, D. et al. The quality of reporting in clinical research: the CONSORT and STROBE initiatives. Aging Clin. Exp. Res. 25, 9–15 (2013).
Google Scholar
Tonzani, S. & Fiorani, S. The STAR methods way towards reproducibility and open science. iScience 24, 102137 (2021).
Google Scholar
Fourquet, J. et al. Disparities in healthcare services in women with endometriosis with public vs private health insurance. Am. J. Obstet. Gynecol. 221, 623.e1–623.e11 (2019).
Google Scholar
Balascio, P. et al. Measures of racism and discrimination in preterm birth studies. Obstet. Gynecol. 141, 69–83 (2023).
Google Scholar
Hong, X., Bartell, T. R. & Wang, X. Gaining a deeper understanding of social determinants of preterm birth by integrating multi-omics data. Pediatr. Res. 89, 336–343 (2021).
Google Scholar
Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).
Google Scholar
Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95–103 (2019).
Google Scholar
Espinosa, C. A. et al. Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries. Sci. Adv. 9, eade7692 (2023).
Google Scholar
Sun, T., He, X. & Li, Z. Digital twin in healthcare: recent updates and challenges. Digit. Health 9, 20552076221149651 (2023).
Google Scholar
